Web-scale taxonomy learning

In this paper, we propose an automatic and unsupervised methodology to obtain taxonomies of terms from the Web and represent retrieved web sites into a meaningful organization for a desired domain without previous knowledge. It is based on the intensive use of web search engines to retrieve domain suitable resources from which extract knowledge, and to obtain web scale statistics from which infer knowledge relevancy. Results can be useful for easing the access to the web resources or as the first step for constructing ontologies suitable for the Semantic Web.

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